This paper examines the predictions of expectations-based reference-dependent models for risk-apportionment tasks that elicit higher-order risk attitudes. We consider some of the most commonly used specifications of Kőszegi and Rabin (2006, 2007) and disappointment aversion models. Our analysis reveals that higher order risky choices exhibited by decision makers defined by those model specifications depend on whether risks to be apportioned in these tasks are symmetric or asymmetric, whether they include small probability outcomes, and on the level of loss aversion. We highlight that some of the predicted choice behaviour in the risk-apportionment tasks differs from the ones in alternative models of decision under risk. We employ experimental data to examine whether choice patterns in the risk apportionment tasks are in line with the predictions of the model specifications described here. We find that only a small proportion of them are consistent with those predictions.

Circular start-ups are vectors of hope for a circular transition. But what does a circular champion’s bankruptcy tell us? That a structural barrier to the circular transition is the total dependence on the ability of circular businesses to be profitable.

The scope for generating high-rank transmuted distributions has expanded beyond the cubic to achieve improved performance in baseline distributions such as those of the Gamma type. This paper develops a Quartic Rank Transmutation Distribution (QRTD), a new family of transmuted distributions with enhanced flexibility for modelling complex data problems, including those with multi-modal distributions. Application is carried out to obtain a transmuted exponential distribution (QTED). Various characteristics of the new exponential distribution are presented, including the cumulative distribution function, the reliability and hazard functions, moments, and relevant order statistics. These features support the legitimacy and robustness of the proposed QTED. Additionally, the paper identifies specific parameter ranges that exhibit notable behaviours in the new distribution and its survival quantities. The maximum likelihood estimates of parameters are described, with simulation studies indicating that their precision improves with larger sample sizes. The performance of the QTED is found to be superior to existing lower-rank cubic and quadratic transmuted exponential distributions based on information criteria using real lifetime data. The applications demonstrate that the high-rank transmutation map could be instrumental in obtaining new transmutations of other relevant distributions with improved performance. This development signifies a major advancement in the field of probability distributions, offering more sophisticated tools for statisticians and researchers to model and analyse complex data patterns more accurately and effectively. Thus, the QRTD and its applications hold significant promise for future research and practical implementations in various statistical and applied fields.

This paper reports on lork currently being undertaken at the University o£ Lancaster into linguistic problems of text to-speech processing. The project is at a very early stage, and will bemaking use of earlier work at Lancaster on the grammatical analysis at written texts.

Artificial intelligence (AI) and technology's possibilities and influence on marketing are crucial and well-known. ChatGPT is a new AI-powered technology that has caused a worldwide boom since its release, with substantial ramifications for all business practises. While acknowledging the growing body of work on ChatGPT, this study attempts to contribute to the ongoing debate by identifying the numerous prospects for employing ChatGPT as an enabler in marketing research and practise. It also highlights the difficulties associated with employing ChatGPT in marketing research. The chapter also discusses the potential for incorporating ChatGPT into other disciplines of marketing, including consumer behaviour, advertising, branding, and sales, as well as the practical consequences for managers. Finally, the chapter recognises the need for more study to better understand the scope of ChatGPT and offers a future research strategy. The chapter concludes by emphasising the significance of incorporating ChatGPT into marketing research and practises with human interaction.

Abstract The glycocalyx is a proteoglycan-rich layer present on the surface of all mammalian cells that is particularly prevalent on endothelial cells lining the vasculature. It has been hypothesized that the glycocalyx mediates leukocyte migration by masking adhesion molecules and reducing leukocyte adhesion to the endothelium. Leukocyte recruitment is a key driver of inflammatory diseases, including the chronic skin disease, psoriasis. Here, we show that leukocytes express heparan sulfate, an important glycocalyx component, on their cell surface which is lost in response to psoriasis-like skin inflammation, whilst endothelial heparan sulfate expression is not affected. Treatment with a heparan sulfate mimetic during psoriasis-like skin inflammation protected heparan sulfate from cleavage by heparanase and resulted in reduced leukocyte accumulation in skin, yet unexpectedly, led to increased clinical signs of inflammation due to reduced Treg numbers. These findings reshape our understanding of immune cell recruitment by revealing the presence and function of a heparan sulfate glycocalyx on immune cells and highlight the complex effects of heparanase inhibitors on the immune response in this context. One Sentence Summary Leukocytes express a glycocalyx on their surface which is shed in response to psoriasis-like skin inflammation, facilitating their migration into the skin.

Federated Graph Learning (FGL) has garnered widespread attention by enabling collaborative training on multiple clients for semi-supervised classification tasks. However, most existing FGL studies do not well consider the missing inter-client topology information in real-world scenarios, causing insufficient feature aggregation of multi-hop neighbor clients during model training. Moreover, the classic FGL commonly adopts the FedAvg but neglects the high training costs when the number of clients expands, resulting in the overload of a single edge server. To address these important challenges, we propose a novel FGL framework, named SpreadFGL, to promote the information flow in edge-client collaboration and extract more generalized potential relationships between clients. In SpreadFGL, an adaptive graph imputation generator incorporated with a versatile assessor is first designed to exploit the potential links between subgraphs, without sharing raw data. Next, a new negative sampling mechanism is developed to make SpreadFGL concentrate on more refined information in downstream tasks. To facilitate load balancing at the edge layer, SpreadFGL follows a distributed training manner that enables fast model convergence. Using real-world testbed and benchmark graph datasets, extensive experiments demonstrate the effectiveness of the proposed SpreadFGL. The results show that SpreadFGL achieves higher accuracy and faster convergence against state-of-the-art algorithms.

PurposeThe study examines the effect of natural resources (NRs) and the control of corruption, voice and accountability and regulatory quality on carbon emissions in Africa. Aside from their individual effects, the moderation effect of institutional quality is assessed.Design/methodology/approachData from 32 African countries from 2002 to 2021 and the fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) regression methods were used for the investigation.FindingsIn the long term, the NRs effect is sensitive to the estimation technique employed. However, quality regulatory framework, robust corruption control and voice and accountability abate any positive effect of NRs on carbon emissions. Institutional quality can be argued to moderate the CO2-emitting potentials of resource extraction in the selected African countries.Practical implicationsEnhancing regulation quality, enforcing corruption control and empowering citizens towards greater participation in governance and demanding accountability are essential catalyst to effectively mitigate CO2 emissions resulting from NRs.Originality/valueThe moderation effect of control of corruption, voice and accountability and regulatory quality on the NR–carbon emission nexus is examined.

Learning penetrates religion in many ways. Primary religious socialisation – sometimes referred to as religious nurture – is the process by which children are explicitly and purposefully taught to do things religiously or they learn implicitly by following what their families and other people around them do, speak and feel. In secondary religious socialisation one sets about learning something additional to or different from what was learned and internalised in one’s religious or non-religious childhood home and surroundings. Secondary socialisation may also entail processes of unlearning something previously learned in order to grasp and master the skills, ways of thinking and feeling, and discursive habits of the new worldview and context of life. Learning in both primary and secondary socialisation can in some cases turn into a thoroughly religiously informed way of life. Alternatively, religious learning may not always be sufficiently lengthy and committed to result in full socialisation. Many people engage in religion merely in special situations or turning points in life, and after that particular need is over, religion loses its acute significance. Furthermore, religious learning may also have intended or unintended effects and consequences beyond the more strictly bounded religious sphere and may come to be reflected in personal and relational life much more widely. (See Long and Hadden 1983; Sherkat 2003; Collet Sabe 2007; Berliner and Sarró 2008; Erricker, Ota and Erricker 2012; Scourfield et al. 2013; Klingenberg, Sjö and Broo 2019.)